MAPPING MODELS FOR FOOD WASTE AND LOSS: FRAMEWORKS, METHODOLOGIES, AND ENVIRONMENTAL IMPACT WITHIN THE WEFE NEXUS

Abstract

Food waste (FW) and food loss (FL) have become global challenges, with severe impacts on the environment, economy, and society. Addressing these issues requires a comprehensive understanding of the drivers, patterns, and potential mitigation strategies across various stages of the food supply chain. This study provides a structured mapping of FW and FL models, offering insights into their methodologies, underlying principles, and applicability across diverse regions and sectors. Our research examines existing models in terms of their approach to data collection, processing, and analysis, focusing on frameworks that quantify FW and FL in terms of volume, economic loss, and environmental impact. By categorizing models based on food supply chain stages—production, processing, distribution, retail, and consumption—we identify key variables influencing FW and FL and highlight the scope and limitations of each model. Additionally, this study addresses emerging models incorporating advanced technologies, such as machine learning and IoT, to predict and manage food waste more accurately. Integrating Life Cycle Assessment (LCA) methodologies, our analysis also emphasizes the environmental impacts of FW and FL, particularly related to carbon emissions, water use, and energy consumption, aligning with the Water-Energy-Food-Ecosystem (WEFE) nexus. This mapping facilitates the identification of critical gaps in FW and FL research and proposes a pathway for developing more holistic, region-specific, and scalable models. By establishing a comprehensive overview of FW and FL models, this study contributes to advancing sustainable food systems, reducing resource wastage, and promoting informed policy-making.